The present invention relates to image segmentation, and more particularly, to segmentation of tubular structures in images.
Tubular structures can appear as strokes or stroke-like structures in 2-dimensional images. As used herein, the term “stroke” refers to variable width curves in 2-dimensional images. Segmentation of stroke-like structures, such as blood vessels, is a fundamental problem in medical imaging, and is an important component of clinical applications involving diagnosis (e.g., stenosis, aneurysm, etc.), surgical planning, anatomical modeling and simulation, and treatment verification. Segmentation of stroke-like structures is a problem that also arises in other contexts including industrial applications and aerial/satellite image analysis.
Using manual segmentation techniques to segment stroke-like structures, it is possible to obtain highly accurate results. However, such manual techniques typically require too much tedious labor to be practical in clinical applications. Accordingly, various fully automatic segmentation methods have been developed for segmenting vessels and other stroke-like structures. However, due to poor contrast, noise, and clutter that is common to medical images, it is often difficult for fully automatic segmentation methods to yield robust results. Furthermore, one may be interested in extracting only a subset, for example a specific path through a branching network of stroke-like structures. Therefore, there is a salient need for an interactive segmentation method that is mostly automatic, but accepts input from an operator to guide the segmentation in a particular direction, quickly correct for errant segmentations, and add branches to an existing segmentation result. Computational efficiency is crucial to such a semi-automatic segmentation method, so that the operator will not have to wait for segmentation results while interacting with data. Typical conventional automatic segmentation techniques have runtimes that are too slow for use in such an interactive segmentation method.